Visual summarization of video for quick understanding by determining emotion objects for semantic segments of video

ABSTRACT

The types and locations of particular types of content in a video are visually summarized in a way that facilitates understanding by a viewer. A method may include determining one or more semantic segments of the video. In addition, the method may include determining one or more emotion objects for at least one of the semantic segments. Further, the method may include generating a user interface on a display screen. The user interface may include one window, and in another embodiment, the user interface may include two windows. Moreover, the method may include displaying first indicia of the emotion object in a first window. The horizontal extent of the first window corresponds with the temporal length of the video and the first indicia are displayed at a location corresponding with the temporal appearance of the emotion object in the video.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of co-pending U.S. patent applicationSer. No. 13/722,754, filed Dec. 20, 2012. The aforementioned relatedpatent application is herein incorporated by reference in its entirety.

FIELD

This disclosure relates generally to graphical user interfaces, and moreparticularly, to visually summarizing a video in a way that facilitatesquick understanding by a viewer of the types and locations of particulartypes of content.

BACKGROUND

A television show, movie, internet video, or other similar content maybe stored on a disc or in other memory using a container or wrapper fileformat. The container format may be used to specify how multipledifferent data files are to be used. The container format for a videomay identify different data types and describe how they are to beinterleaved when the video is played. A container may contain videofiles, audio files, subtitle files, chapter-information files, metadata,and other files. A container also typically includes a file thatspecifies synchronization information needed for simultaneous playbackof the various files.

One format for digital video files is the DVD-Video format. Anotherformat for digital video files is Audio Video Interleaved (“AVI”). Audiomay be stored in various formats, such as the PCM, DTS, MPEG-1 AudioLayer II (MP2), or Dolby Digital (AC-3) formats.

A multimedia video generally includes a large amount of perceptualinformation, i.e., information such as images and sounds that areperceived by viewers. The frames of a video file may show humans, whomay or may not be actors, and a wide variety of nonhuman objects. Anonhuman object may be a background, such as a natural indoor or outdoorlocation, or a professional stage or set. A nonhuman object may also bea prop or other visual element in front of the background object. Yetanother type of nonhuman object that may be shown in a video frame istext. For instance, words spoken by humans may be displayed as text in aparticular area of the frames. Segments of an audio file may besynchronously played with the display of video frames. These segmentsmay include spoken words, music, and a wide variety of sound effects.

While an audio-video file may be as short as a few minutes, the typicalvideo, such as a television show or a full length movie, ranges inlength from 20 minutes to over two hours. The typical video may includemany scenes, each corresponding with a particular segment of the video.For example, a movie may have between 50 and 200 scenes. A minor scenemay be one minute or less. A major scene may be three or more minutes.Each scene may include many frames and may include one or more camerashots. A scene may be accompanied by spoken dialog, a particular musicalscore or set of sound effects, or a combination of sound types.Particular human and nonhuman objects may appear in a scene. A scene maybe intended by the creator to invoke particular emotions or moods, or toconvey a theme of the story.

SUMMARY

One embodiment is directed to a method that visually summarizes thetypes and locations of particular types of content in a video in a waythat facilitates understanding by a viewer. The method may includedetermining one or more semantic segments of the video. In addition, themethod may include determining one or more emotion objects for at leastone of the semantic segments. Further, the method may include generatinga user interface on a display screen. The user interface may include onewindow, and in another embodiment, the user interface may include twowindows. Moreover, the method may include displaying first indicia ofthe emotion object in a first window. The horizontal extent of the firstwindow corresponds with the temporal length of the video and the firstindicia are displayed at a location corresponding with the temporalappearance of the emotion object in the video.

Additional embodiments are directed to a non-transitorycomputer-readable storage medium having executable code stored thereonto cause a machine to perform a method for rendering a summary of avideo, and to a system that visually summarizes the types and locationsof particular types of content in a video in a way that facilitatesunderstanding by a viewer.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts a high-level block diagram of an exemplary computersystem for implementing various embodiments.

FIG. 2 is a block diagram of an exemplary audio-visual file containeraccording to one embodiment.

FIG. 3 is a block diagram of a process for visually summarizing a videoin a way that facilitates quick understanding by a viewer of the typesand locations of particular types of content according to an embodiment.

FIG. 4 depicts a display screen displaying a user interface according toone embodiment.

FIG. 5 illustrates one embodiment of a process for generating visualtags according to one embodiment.

FIG. 6 illustrates a process for generating audio and key word tagsaccording to one embodiment.

FIG. 7 depicts a display screen displaying a user interface according toan embodiment.

FIG. 8 depicts a display screen displaying a user interface according toan embodiment.

DETAILED DESCRIPTION

A multimedia video generally includes a large amount of perceptualinformation, i.e., information such as images and sounds that may beperceived by viewers. For example, a video may show human and nonhumanobjects. A video may include spoken words, music, and other sounds,which may be referred to herein as audio objects. A video may evokevarious emotions, moods, or themes, which may be referred to herein asemotion objects. The spoken words may include “key words.” A key wordmay be a word that provides significant information content about ascene in a video. These objects and key words may be used to describe ascene to a viewer. In particular, according to various embodiments,visual representations of key words, and human, nonhuman, audio, andemotion objects may be used to describe the scenes of a video to aviewer. In addition, visual representations of the relationships betweenthese objects and key words may be used to describe the scenes of avideo to a viewer. By visually presenting this information to theviewer, he or she may be enabled to generally understand the scene. Theinformation may enable the viewer to determine whether a particularscene is of interest or is objectionable. In various embodiments, visualinformation summarizing all of the scenes of a video may be presented tothe viewer in a single display screen.

According to various embodiments, a viewer selects a video, and human,nonhuman, and audio objects of the video are identified. In addition,key words that are spoken by human objects in the video are identified.Human, nonhuman, and audio objects may be used to classify a particularsegment of a video as a scene. The objects and key words are thenassociated with the scenes of the video. Further, the objects, keywords, and other data may be used to determine an emotion, mood, ortheme for one or more of the scenes, and to generate correspondingemotion objects. The objects and key words may be compared with profileinformation to determine an attitude or preference of a viewer regardingthe scenes of the video. A viewer's attitude may be, for example, thathe or she likes, dislikes, or finds a particular type of contentobjectionable. In various embodiments, visual representations of keywords, and human, nonhuman, and audio objects summarizing all of thescenes of a video are presented to the viewer in a single displayscreen. In addition, visual representations of a viewer's attitudes orpreferences toward a particular object or key word may be displayed.

In one embodiment, a display screen may include a first window forplaying the video and a second window for rendering text, symbols, andicons corresponding with human, nonhuman, audio, and emotion objects,and key words. The second window may also include a visual indication ofa viewer's attitude regarding particular human, nonhuman, audio, andemotion objects, and key words. In one embodiment, a viewer may selectone or more scenes for playing in the first window. One or more otherscenes of the video may be identified as scenes to be recommended to theviewer. The recommended scenes may be other scenes that have human,nonhuman, audio, and emotion objects, and key words that are similar tothe scene selected by the viewer.

FIG. 1 depicts a high-level block diagram of an exemplary computersystem 100 for implementing various embodiments. The mechanisms andapparatus of the various embodiments disclosed herein apply equally toany appropriate computing system. The major components of the computersystem 100 include one or more processors 102, a memory 104, a terminalinterface 112, a storage interface 114, an I/O (Input/Output) deviceinterface 116, and a network interface 118, all of which arecommunicatively coupled, directly or indirectly, for inter-componentcommunication via a memory bus 106, an I/O bus 108, bus interface unit109, and an I/O bus interface unit 110.

The computer system 100 may contain one or more general-purposeprogrammable central processing units (CPUs) 102A and 102B, hereingenerically referred to as the processor 102. In an embodiment, thecomputer system 100 may contain multiple processors typical of arelatively large system; however, in another embodiment, the computersystem 100 may alternatively be a single CPU system. Each processor 102executes instructions stored in the memory 104 and may include one ormore levels of on-board cache.

In an embodiment, the memory 104 may include a random-accesssemiconductor memory, storage device, or storage medium (either volatileor non-volatile) for storing or encoding data and programs. In anotherembodiment, the memory 104 represents the entire virtual memory of thecomputer system 100, and may also include the virtual memory of othercomputer systems coupled to the computer system 100 or connected via anetwork. The memory 104 is conceptually a single monolithic entity, butin other embodiments the memory 104 is a more complex arrangement, suchas a hierarchy of caches and other memory devices. For example, memorymay exist in multiple levels of caches, and these caches may be furtherdivided by function, so that one cache holds instructions while anotherholds non-instruction data, which is used by the processor orprocessors. Memory may be further distributed and associated withdifferent CPUs or sets of CPUs, as is known in any of various so-callednon-uniform memory access (NUMA) computer architectures.

The memory 104 may store all or a portion of the following: an audiovisual file container 150 (shown in FIG. 2 as container 202), a videoprocessing module 152, an audio processing module 154, and a controlmodule 156. These modules are illustrated as being included within thememory 104 in the computer system 100, however, in other embodiments,some or all of them may be on different computer systems and may beaccessed remotely, e.g., via a network. The computer system 100 may usevirtual addressing mechanisms that allow the programs of the computersystem 100 to behave as if they only have access to a large, singlestorage entity instead of access to multiple, smaller storage entities.Thus, while the audio visual file container 150, video processing module152, audio processing module 154, and control module 156 are illustratedas being included within the memory 104, these components are notnecessarily all completely contained in the same storage device at thesame time. Further, although the audio visual file container 150, videoprocessing module 152, audio processing module 154, and control module156 are illustrated as being separate entities, in other embodimentssome of them, portions of some of them, or all of them may be packagedtogether.

In an embodiment, the video processing module 152, audio processingmodule 154, and control module 156 may include instructions orstatements that execute on the processor 102 or instructions orstatements that are interpreted by instructions or statements thatexecute on the processor 102 to carry out the functions as furtherdescribed below. In another embodiment, the video processing module 152,audio processing module 154, and control module 156 are implemented inhardware via semiconductor devices, chips, logical gates, circuits,circuit cards, and/or other physical hardware devices in lieu of, or inaddition to, a processor-based system. In an embodiment, the videoprocessing module 152, audio processing module 154, and control module156 may include data in addition to instructions or statements.

The video processing module 152 may include various processes thatgenerate visual tags according to one embodiment. The audio processingmodule 154 may include various processes for generating audio and keyword tags according to one embodiment. The control module 156 mayinclude various processes for visually summarizing a video in a way thatfacilitates quick understanding by a viewer of the types and locationsof particular types of content according to an embodiment. In addition,the control module 156 may include various processes for rendering allor selected portions of a video, and rendering a user interface, such asthe one shown in FIG. 4. Further, the control module 156 may includevarious processes for identifying scenes to be recommended to a viewer,as well as other processes described herein.

The computer system 100 may include a bus interface unit 109 to handlecommunications among the processor 102, the memory 104, a display system124, and the I/O bus interface unit 110. The I/O bus interface unit 110may be coupled with the I/O bus 108 for transferring data to and fromthe various I/O units. The I/O bus interface unit 110 communicates withmultiple I/O interface units 112, 114, 116, and 118, which are alsoknown as I/O processors (IOPs) or I/O adapters (IOAs), through the I/Obus 108. The display system 124 may include a display controller, adisplay memory, or both. The display controller may provide video,audio, or both types of data to a display device 126. The display memorymay be a dedicated memory for buffering frames of video data. Thedisplay system 124 may be coupled with a display device 126, such as astandalone display screen, computer monitor, television, or a tablet orhandheld device display. In one embodiment, the display device 126 mayinclude one or more speakers for rendering audio. Alternatively, one ormore speakers for rendering audio may be coupled with an I/O interfaceunit. In alternate embodiments, one or more of the functions provided bythe display system 124 may be on board a processor 102 integratedcircuit. In addition, one or more of the functions provided by the businterface unit 109 may be on board a processor 102 integrated circuit.

The I/O interface units support communication with a variety of storageand I/O devices. For example, the terminal interface unit 112 supportsthe attachment of one or more viewer I/O devices 120, which may includeviewer output devices (such as a video display device, speaker, and/ortelevision set) and viewer input devices (such as a keyboard, mouse,keypad, touchpad, trackball, buttons, light pen, or other pointingdevice). A viewer may manipulate the user input devices using a userinterface, in order to provide input data and commands to the user I/Odevice 120 and the computer system 100, and may receive output data viathe user output devices. For example, a user interface may be presentedvia the user I/O device 120, such as displayed on a display device,played via a speaker, or printed via a printer.

The storage interface 114 supports the attachment of one or more diskdrives or direct access storage devices 122 (which are typicallyrotating magnetic disk drive storage devices, although they couldalternatively be other storage devices, including arrays of disk drivesconfigured to appear as a single large storage device to a hostcomputer, or solid-state drives, such as flash memory). In anotherembodiment, the storage device 122 may be implemented via any type ofsecondary storage device. The contents of the memory 104, or any portionthereof, may be stored to and retrieved from the storage device 122 asneeded. The I/O device interface 116 provides an interface to any ofvarious other I/O devices or devices of other types, such as printers orfax machines. The network interface 118 provides one or morecommunication paths from the computer system 100 to other digitaldevices and computer systems; these communication paths may include,e.g., one or more networks.

Although the computer system 100 shown in FIG. 1 illustrates aparticular bus structure providing a direct communication path among theprocessors 102, the memory 104, the bus interface 109, the displaysystem 124, and the I/O bus interface unit 110, in alternativeembodiments the computer system 100 may include different buses orcommunication paths, which may be arranged in any of various forms, suchas point-to-point links in hierarchical, star or web configurations,multiple hierarchical buses, parallel and redundant paths, or any otherappropriate type of configuration. Furthermore, while the I/O businterface unit 110 and the I/O bus 108 are shown as single respectiveunits, the computer system 100 may, in fact, contain multiple I/O businterface units 110 and/or multiple I/O buses 108. While multiple I/Ointerface units are shown, which separate the I/O bus 108 from variouscommunications paths running to the various I/O devices, in otherembodiments, some or all of the I/O devices are connected directly toone or more system I/O buses.

In various embodiments, the computer system 100 is a multi-usermainframe computer system, a single-user system, or a server computer orsimilar device that has little or no direct user interface, but receivesrequests from other computer systems (clients). In other embodiments,the computer system 100 may be implemented as a desktop computer,portable computer, laptop or notebook computer, tablet computer, pocketcomputer, telephone, smart phone, or any other suitable type ofelectronic device.

FIG. 1 is intended to depict the representative major components of thecomputer system 100. Individual components, however, may have greatercomplexity than represented in FIG. 1, components other than or inaddition to those shown in FIG. 1 may be present, and the number, type,and configuration of such components may vary. Several particularexamples of additional complexity or additional variations are disclosedherein; these are by way of example only and are not necessarily theonly such variations. The various program components illustrated in FIG.1 may be implemented, in various embodiments, in a number of differentmanners, including using various computer applications, routines,components, programs, objects, modules, data structures, etc., which maybe referred to herein as “software,” “computer programs,” or simply“programs.”

FIG. 2 is a block diagram of an exemplary audio-visual file container202 that may contain a video file 204, an audio file 206, a subtitlefile 208, and a metadata file 210 according to one embodiment. Thecontainer may also include other files, such as a file that specifiessynchronization information.

FIG. 3 is a block diagram of a process 300 for visually summarizing avideo in a way that facilitates quick understanding by a viewer of thelocations of particular types of content according to an embodiment. Theprocess 300 may receive as input a visual tag file 302, an audio tagfile 304, a key word tag file 306, an attribute tag file 308, and ametadata file 210. The visual tag file 302 includes tags that correspondwith visually perceivable objects, such as human and nonhuman objects.The audio tag file 304 includes tags that correspond with aurallyperceivable objects. The key word tag file 306 includes tags thatcorrespond with key word objects. The attribute tag file 308 includestags that correspond with attribute objects. Each tag may be associatedwith a time stamp that indicates the start and stop time in which theobject or attribute is rendered or otherwise associated. Exemplaryembodiments for automatically determining tags are described below withreference to FIGS. 5-6. In addition, in some embodiments, tags of one ormore types may be wholly or partially determined using manual methods.

The operation 310 may include comparing a tag with one or more othertags associated with the same shot or scene for consistency. A shot maybe a continuous sequence of frames captured without interruption by acamera oriented in a single direction or camera angle. As one example, avisual tag may indicate that a particular human object appears in a shotand a key word tag identifying the name of the human object isassociated with the shot. As another example, a visual tag may indicatethat a particular human object appears in a shot and an audio tagidentifies an audio signature of the human object is associated with theshot. In these examples, if the tags that are compared indicate the sameobject, the positive or consistent result of the comparison may be usedin operation 310 to validate that the human object was correctlyidentified. If there are no tags that are consistent with a particulartag, it may be determined that the object associated with the particulartag was misidentified. The operation 310 may include modifying a tagdetermined to be inconsistent with other tags associated with the sameshot. The modification may include adding an indication to the tag thatit should not be used in other processes. Alternatively, if aprobability or confidence parameter associated with the particular tagis above a threshold, it may be determined that the object was correctlyidentified and that the shot or scene includes multiple objects. In thiscircumstance, the modification may include adding an indication to thetag that it may be relied on to a particular extent.

In operation 312, an emotion tag file 314 may be created from theattribute tag file 308 and the consistency-corrected visual tag 302,audio tag 304, key word tag 306, and metadata 210 files. The emotion tagfile 314 includes tags that are associated with emotion objects. In oneembodiment, an emotion object may be associated with an emotion, mood,or theme that a typical viewer might be expected to perceive or that thecreators of a video intended the audience to perceive. Each emotionobject may be of a predefined type and associated with a time stamp. Anemotion object may include parameters corresponding with intensity ofthe perceived emotion or a confidence level that the perceived emotionaccurately represents a ground truth emotion. An emotion object may begenerated directly from the attribute file 308, such as where theattribute file identifies an association or correlation of an attributewith a perceived emotion. In addition, an emotion object may begenerated directly from the visual tag 302, such as where the tagidentifies a human object displaying a particular emotion. Further, anemotion object may be generated directly from the audio tag 304 or keyword tag 306 files, such as where an audio tag identifies a segment ofsound associated or correlated with an emotion, mood, or theme, or a keyword is associated with an emotion, mood, or theme. Moreover, an emotionobject may be generated in operation 312 by identifying patterns ofvisual, audio, key word, and attribute tags that correspond or correlatewith an emotion object. Further, an emotion object may be generated inoperation 312 using contextual data provided in the metadata file 210,such as metadata designating that the video is of a particular genre,e.g., comedy, horror, drama, or action. For example, visual, audio, andattribute tags for a shot or scene may all be associated with aparticular mood, e.g., amusement, fear, sadness, suspense, or interest.In one embodiment, an emotion object may be determined using manualmethods. In one embodiment, a tag may be generated for an emotionobject. An emotion tag may include an intensity level of the emotion,mood, or theme. In addition, in one embodiment, a single emotion tag maybe associated with two or more emotion objects. For example, a typicalviewer might be expected to simultaneously perceive two emotions, suchas happiness and surprise, when perceiving a particular scene. In arendering operation 316, one or more tags of the tag files 302, 304,306, 308, and 314 may be rendered as one or more indicia on a displaydevice according to known techniques.

FIG. 4 depicts a display screen 402 of a display device, e.g., display126 (FIG. 1), for displaying a user interface. In one embodiment, theuser interface includes windows 404 and 406, which may be rendered onthe display screen 402 along with a variety of textual information, andcontrol icons or buttons, e.g., buttons 403, 405, outside of thewindows. The video may be played in the window 404. A variety of text,symbols, lines, and icons (“indicia”) for summarizing the video may berendered in the window 406. The horizontal extent of the window 406 maycorrespond with the duration or total time of the video. The x axisshown in the figure represents the horizontal extent or time, while they axis represents a vertical direction. While FIG. 4 depicts a userinterface that includes a window 404 for playing a video, in otherembodiments, a user interface may omit the window 404, i.e., in otherembodiments, a user interface may include only the window 406 forrendering text, symbols, and icons for summarizing the video (along withcontrol icons or buttons 403, 405 outside of the window 406).

In one embodiment, one or more object identifiers 408 may be rendered onthe display screen 402, such as to one side or the other of the window406, e.g., OBJECT 1 to OBJECT 8. In various embodiments, one or morehorizontal lines (time lines) having a length (or horizontal extent) andtemporal position may be rendered horizontally adjacent to each objectidentifier. The length or horizontal extent may indicate the duration ofthe rendering of the associated object. In FIG. 4, for instance, OBJECT1 is associated with lines 410 a and OBJECT 5 is associated with lines410 e. In the example, it can be seen that OBJECT 1 appears from time t3to time t4, from time t5 to time t6, from time t7 to time t8. Incontrast, OBJECT 3 appears from time t1 to time t2 and does not appearagain. In one embodiment, an icon rather than a line may be rendered toindicate the temporal location of an object. For example, an icon 418may be displayed to show where an audio object associated with music islocated. In embodiments where an icon is rendered to indicate thetemporal location of an object and the horizontal extent of the icon issmaller than the duration of the rendering of the object, the icon maybe rendered at a point corresponding with the start of the time periodin which the object is rendered. Alternatively, the icon may be renderedat a point corresponding with the midpoint or end of the time period inwhich the object is rendered. Exemplary embodiments for automaticallydetermining object identifiers are described below with reference toFIGS. 5 and 6. It will be appreciated that the horizontal lines 410a-410 h facilitate a quick understanding by a viewer of the types andlocations of various objects in the video. In addition, a viewer mayquickly understand where different objects simultaneously appear in thevideo. For example, OBJECTS 4 and 5, which may be two particular actors,only appear together in the final quarter of the video. Further, aviewer may quickly understand where preferred or objectionable objectsappear in the video. For example, horizontal lines for objectionableobjects may be rendered in a different color than the color used forhorizontal lines for objects generally.

Still referring to FIG. 4, in various embodiments, key words 414 (“KW#”)may be rendered in the second window 406 at horizontal locationscorresponding with the temporal rendering of the particular key word inthe video. For example, in FIG. 4, it may be seen that key word 1 (KW1)414 appears in the video at the start, at approximately the one-thirdtime point, at approximately the two-thirds time point, and at a timepoint about eighty percent of the way through the video. A key word 414may be rendered at any desired vertical coordinate or position withinthe second window 406, i.e., it may but need not be associated with oneof the object identifiers 408. Exemplary embodiments for automaticallydetermining key words are described below with reference to FIG. 6. Itwill be appreciated that the display of key words 414 facilitates aquick understanding by a viewer of the types and locations of variouskey words in the video. In addition, a viewer may quickly understandwhere key words simultaneously occur with the appearance of variousobjects in the video. For example, key word KW4 occurs simultaneouslywith an appearance of object 8.

In various embodiments, as shown in FIG. 4, emotion, mood, or themedenoting icons 416 may be rendered in the second window 406. An emotiondenoting icon 416 may be associated with and representative of anemotion tag. An emotion denoting icon 416 may be rendered at horizontallocations corresponding with the temporal location of the particularemotion tag in the video. In one embodiment, an emotion or mood denotingicon 416 may be an “emoticon.” In other embodiments, an emotion or mooddenoting icon 416 may be a colored or gray-scale icon. While depicted ascircular, an icon 416 may be any shape. In various embodiments, thesize, color, or shade of an icon 416 may correspond with an intensity ofthe associated emotion tag. For example, an icon 416 associated withamusement or a funny mood may be relatively large if the mood or emotionwould be expected to be perceived intensely, but the same icon may berelatively small if the mood or emotion would be expected to beperceived mildly. It will be appreciated that the display of emotiondenoting icons 416 facilitates a quick understanding by a viewer of thetypes and locations of various emotions, moods, or themes in the video.A viewer can determine in a single view the proportion of the video thatis associated with a particular emotion, mood, or theme, such as actionor comedy. In addition, a viewer can determine in a single view whereemotion objects of a particular type are located, e.g., funny portionsof the video.

FIG. 5 illustrates of a process for generating visual tags according toone embodiment. Referring to FIG. 5, in operation 504, a video file 204may be parsed into shot files. As mentioned, a shot may be a continuoussequence of frames captured without interruption by a camera oriented ina single direction or camera angle. During a shot, the camera may have asingle field of view and field size, or may have a variable field ofview, such as a zoom-in or -out shot. The camera may remain fixed, or bemoved in a panning, tilting, or tracking motion. For example, a fixedfield of view shot may be a long shot, a full shot, a medium shot, or aclose up shot.

The video file 204 may be parsed into shot files according to any knownmethod. For example, in one embodiment, a histogram may be computed foreach frame of the video file and the histograms for consecutive framescompared. If the histogram intersection of first and second consecutiveframes is greater than a threshold, it may be inferred that the framesare similar, and consequently that the two frames are part of the sameshot. On the other hand, if the histogram intersection of first andsecond consecutive frames is less than the threshold, it may be inferredthat the two frames form a shot boundary. In addition, it may beinferred that the first consecutive frame is the last frame of apreceding shot and the second consecutive frame is the first frame of asucceeding shot. In one alternative, the histograms of two or moreconsecutive first frames may be compared with the histograms of two ormore consecutive second frames (the group of first and second framesbeing consecutive), and a shot boundary may be defined by moreconsecutive frames than merely two frames. For example, the shottransition between shots may be a “fade” rather than a “cut.” A timecode and type of shot transition (fade or cut) may be recorded asmetadata for use in content analysis described below. Other knownmethods for parsing a video file into shot files may be employed inoperation 504. In addition, operation 504 may include parsing the videofile so that sequential frames between determined shot boundaries aregrouped together or otherwise identified or tagged as being associatedwith a particular shot. Sequential frames associated with a particularshot may be referred to herein as a shot file.

In operation 506, a key frame may be determined for a shot file. The keyframe may be deemed to be representative of all frames in the shot,permitting descriptive data for the shot to be determined only for thekey frame and not for every frame of the shot. In one embodiment, a keyframe may be determined for each shot file. In another embodiment, theoperation 506 of determining a key frame may be omitted. Any knownmethod for determining a key frame may be employed. In one embodiment, akey frame may be determined by selecting a middle frame of the shotfile. In alternative embodiments, descriptive data for the shot may bedetermined for each of two or more key frames for a shot. Other knownmethods for determining a key frame may be employed in operation 506.

In operation 508, various shot attributes may be determined and recordedas metadata. Examples of shot attributes may include shot length, colorvariance, type of illumination or lighting, amount of motion, and shottype (zooming, panning, tilting, tracking motion, long, full, medium, orclose up). Shot length may be determined by counting the number offrames of a shot. Color variance and illumination or lighting propertiesmay be determined by analyzing pixel values of key frames using knowntechniques. The amount of motion may be determined by evaluating thenumber of times individual pixels change value from frame-to-frame in ashot using known techniques. Shot type may be determined using knowntechniques. A shot attribute may correspond with known cinematictechniques for evoking a particular mood. For example, particularlighting may be used to evoke a suspense theme. Metadata for a shot mayinclude mood, emotion, or theme where another shot attribute isassociated with a known cinematic technique for evoking the mood,emotion, or theme.

In operation 510, visual objects in a shot may be identified and tagged.In one embodiment, visual objects in a shot may be identified byapplication of one or more known image recognition processes to theshot. The operation 510 may operate on one or more key frames of theshot. A shot may include the human and nonhuman visual objects. Bothhuman and nonhuman visual objects may be identified in operation 510.With respect to human visual objects, in one embodiment, a human visualobject may be identified by identifying a face (“human facial object”)in a frame. The operation 510 may include determining whether or not aparticular visual object is present in a shot and, if present, toidentify its location in the frame. The operation 510 may includeextracting an identified object for further processing. For example, anextracted human facial object may be further processed to determine theidentity of the viewer or to determine a facial expression of theviewer.

In operation 510, the position or location within a frame of an objectmay be determined using any known method. For example, a method may beof a type that employs rules that code typical attributes of the object.Attributes of a facial object may include, for example, eyes, eye brows,nose, hair line, hair texture, lips, and mouth. For instance, in thecase of a human facial object, a rule may identify a face only if aparticular facial feature, e.g., a first eye, is in a prescribedrelationship to another feature, e.g., a second eye. In addition, amethod may be of a type that employs rules that identify so-called“invariant features” that are present in a frame regardless of theposition or pose of the object, the lighting, or camera viewpoint.Methods of this type, especially when employed to identify a humanfacial object, may employ an image recognition processes thatidentifies: (i) facial features using edge detectors (e.g., a Sobelfilter) and templates; (ii) skin or hair texture using a neural network;and (iii) skin color using a pixel chrominance classifier. Further,methods may employ multiple techniques in stages, such as identifyingglobal features such as skin color and face shape first, then verifyingthat the region is in fact a face by locating and detecting particularfacial features within the region.

Further, once the position within a frame of an object is determined,the object may be identified as an object of a particular type orinstance using any known method in operation 510. Continuing the exampleof a human facial object, known template matching methods may beemployed. In a first type of template matching method, several standardpatterns of a face are used. The standard patterns may describe the faceas a whole or the facial features separately. Correlations between animage extracted from a frame and the standard patterns may be computed.If the correlations are statistically significant, it may be determinedthat a human facial object is found. In a second type of templatematching method, the patterns are “learned” from training images usingknown statistical analysis and machine learning techniques. In variousembodiments, patterns may be learned from training images using: (i)Eigenfaces; (ii) Distribution-based Methods (including PrincipleComponent Analysis, Factor Analysis, and Fisher's Linear Discriminant);(iii) Neural Networks; (iv) Support Vector Machines; (v) Sparse Networkof Winnows (SNoW); (vi) Naive Bayes Classifiers; (vii) Hidden MarkovModels; (viii) Information-Theoretical Approaches (including Kullbackrelative information); and (ix) Inductive Learning Algorithms.

While methods for object location and identification have been describedwith respect to a human facial object, it will be appreciated that thesetechniques may be generally employed with non-facial human objects andnonhuman objects. For example, a nonhuman object, such as a prop may beidentified though color values and object-specific features. Patternsand templates for nonhuman objects will be different than those forfacial objects. For example, a musical instrument, such as an acousticguitar, may be identified by determining regions of pixels having woodcolor values. Appropriately colored pixel regions may then be comparedwith patterns or templates for neck and body parts of the acousticguitar, as viewed in different orientations.

In one embodiment, a human facial object may be processed to determinethe emotion expressed on the facial object. To determine the emotion ofa facial object, a process may, in one embodiment, employ a Gabor filterto determine facial features and their orientation, and a support vectormachine to determine an emotion corresponding with detected facialfeatures. In one embodiment, a sequence of frames in which a facialexpression morphs from one emotion to another may be analyzed todetermine an emotional category of a human facial object. The sequenceof frames need not include every consecutive frame, e.g., two or morekey frames may be analyzed. The sequence of frames may be analyzed usinga Tree-Augmented-Naive Bayes classifier. In addition, a category ofemotion may be determined by comparing motion vectors with a template.The motion vectors may be based on deformation of facial features asreflected in an optical flow that occurs in a sequence of frames.Optical flow may be determined using differential, matching, energy-, orphase-based techniques. In various embodiments, motions that may bedetermined may include amusement, joy, anger, disgust, embarrassment,fear, sadness, surprise, and a neutral state. Other emotions or moodsmay be determined in alternative embodiments. The operation 510 mayinclude associating a determined emotion with a human object. Inaddition, the operation 510 may include generating an emotion tag thatis associated with the scene of the video in which the facial emotionwas detected. In other embodiments, the emotion of a facial object maybe determined in operation 510 using any known method.

While the amount of motion in a shot may be determined in operation 508,in one embodiment, the amount of motion in a shot may be determined inoperation 510 after identifying an object. For example, the position ofthe identified object in various key frames between the beginning andending frames of the shot may be compared.

Another type of nonhuman object that may be determined in operation 510may be a background, such as such indoor or outdoor location set. Abackground nonhuman object may be determined using known techniques,including techniques that consider the size (number of pixels), color,and distribution of pixels in a frame. A background object may beidentified using a pattern matching technique that employs patterns ortemplates of various background objects. Training images for developinga template may be learned from training images in the video or in ametadata file. In other embodiments, a background object may bedetermined in operation 510 using any known method.

According to an aspect, a segment of two or more video frames thatincludes common objects, that is intended to convey common emotionalcontent, that is intended to convey an element of a story, that isaccompanied by a common audio segment, or some combination of theforegoing may be classified as a scene. A scene may also be referred toin this description and the claims as a “semantic segment.” One or moreof the various tags described herein may be associated with a particularscene or semantic segment if the particular tag is determined fromcontent in the scene.

In operation 512, a visual scene may be determined according to anyknown method. A visual scene may include one or more camera shots andone or more human and nonhuman objects. In one embodiment, scenes may bedetermined by grouping together consecutive shots having visual or audioobjects corresponding with the same ground truth. For example, twoconsecutive shots having the same background object or other non-humanobject may be grouped together as a scene. As another example, a scenemay include a first shot that is a long shot of a particular person anda second shot that is a medium shot of the same person. As a thirdexample, a sequence of four consecutive shots in which the first andthird shots have a first human object and the second and fourth shotshave a second human object may be grouped together as a scene.

In one embodiment, visual scenes may be determined if a preceding andfollowing shot include related visual objects. For example, the firstshot may include a particular person, the second shot may includeanother person, and two may be interacting. In one embodiment, visualscenes may be determined by comparing histogram data. For example,histogram data for a first of three consecutive shots is compared withthe third shot in the series. If the intersection of first and thirdconsecutive shots is outside a threshold, it may be inferred that theshots are similar and part of the same scene, such as where the videoshows an interaction between person A and person B, the camera firstcapturing person A, second capturing person B, and third capturingperson A.

The determination of a visual scene in operation 512 may includeassociating the scene with a probability or confidence parameter that isa measure of how likely the identified and grouped shots define a sceneaccording to a ground truth specifying the temporal boundaries of ascene. In one embodiment, the validity of a scene determined inoperation 512 may be tested by comparing the temporal span of the scenewith other scene determiners, such as a temporal span associated with anaudio object.

The determination of a visual scene in operation 512 may includeassociating an attribute tag with the scene. The attribute tag maycorrespond with known cinematic techniques for evoking a particularmood, e.g., amusement, fear, sadness, suspense, or interest. In oneembodiment, an attribute tag designating an action theme may beassociated with a scene with a relatively large number of shots of shortduration.

In operation 514, visual tags may be associated or set for each scene.As mentioned, a visual tag corresponds with visual objects, such ashuman and nonhuman objects. When a tag is generated, it may beassociated with a time or time span. However, the segments of the videothat correspond with the various scenes may not be known at the time atag is generated. Operation 514 may be performed at a time when thevarious scenes of the video are known so that a previously generatedvisual tag may be associated with a particular scene.

FIG. 6 illustrates a process for generating audio and key word tagsaccording to one embodiment. Referring to FIG. 6 in operation 602, oneor more audio features or audio signal descriptors may be extracted froman audio file 206. An audio feature may be a time domain feature, suchas zero crossing rate, energy contour, volume contour, or fundamentalfrequency, or a frequency domain feature, such as short term energy,bandwidth, entropy, spectral centroid, Mel-Frequency CepstralCoefficients, or a Discreet Wavelet Transform. Many audio features areknown in the art and any known audio feature or features that aresuitable may be extracted in operation 602.

In operation 604, audio features or audio signal descriptors extractedfrom an audio file 206 may be classified. Each classification may bedefined by a set of characteristic audio feature values. In oneembodiment, audio features may be classified as silence, speech (spokenwords), music, and a fourth category of other sounds that will bereferred to herein as “sound effect.”

Segments of the video for which sound is not detectable may beclassified as silent. In operation 605, an audio tag with a silent typeattribute may be associated with a silent audio feature, the tag havinga time stamp that indicates the start and stop time of the silentperiod.

Segments of the video for which the audio feature values are similar tothose that are characteristic of speech may be classified as speech. Anaudio tag with a speech type attribute may be associated with the audiofeature, the tag having a time stamp of the period of speech. Segmentsof the video for which the audio feature values are similar to thosethat are characteristic of music may be classified as music. An audiotag with music type attribute may be associated with the audio feature,the tag having a time stamp of the period of music.

Segments of the video for which the audio feature values are not similarto those that are characteristic of speech or music (and are not silent)may be classified as a sound effect. An audio tag with sound effect typeattribute may be associated with a time stamp of the period of music.The sound effect category may include sounds conventionally understoodto be movie or television sound effects, such as an explosion, a doorbeing slammed, a motor vehicle engine, a scream, laughter, applause,wind, and rain. The sound effect category may include any sound that maynot be classified as speech, music, or silence, even if the sound maynot be conventionally understood to be a theatrical sound effect.

In operation 606, audio features classified as sound effects may befurther classified by sound effect type. Each sound effectsub-classification may be defined by a set of characteristic audiofeature values. For example, a gun shot may be defined by particularaudio feature values. A library of audio feature values that arecharacteristic of a variety of sound effects may be provided. Each audiofeature classified as a sound effect may be compared with the library ofcharacteristic features. Where matches are found, the sound effect audiotag may have additional data added to it, specifying the particularsound, e.g., a crying baby sound effect.

An optional operation 607 may include associating an attribute tag witha sound effect audio feature. The attribute tag may correspond withknown cinematic techniques for evoking a particular mood. In oneembodiment, an attribute tag designating an action theme may beassociated with gun shot or explosion sound effects. In otherembodiments, an attribute tag designating a suspense theme or amusementtheme may be associated with a sound effect.

In operation 608, an audio or acoustic fingerprint may be determined foraudio features classified as music. An audio fingerprint is acontent-based compact signature that may summarize a music recording. Inone embodiment, an audio fingerprint does correspond with an exact copyof a particular music recording. An audio fingerprint may be found tomatch an extracted music recording where small variations from theparticular music recording are present in the extracted audio features.An audio fingerprint is derived from the extracted audio features andmay include a vector, a trace of vectors, a codebook, a sequence ofHidden Markov model sound classes, a sequence of error correcting words,or musically meaningful high-level attributes.

A library of audio fingerprints for various music recordings may beprovided. In operation 610, audio features classified as music may becompared with the library. Where matches are found, the music audio tagmay have additional data added to it, specifying an identification ofthe particular song. In addition, an attribute tag designating anemotion, mood, or theme may be associated with a music audio tag.Particular cinematic techniques are known to employ certain types ofmusic to evoke particular moods. In one embodiment, a music audio tagmay include attribute data designating that the music is associated withaction, suspense, or sad themes if the music is of a particular type.

In operation 612, an audio transcript may be determined. An audiotranscript may include all of the words spoken in the video. In oneembodiment, an audio transcript may be provided with the video in theform of a closed caption file included in the AV file container. Inanother embodiment, spoken words may be determined from audio featuresclassified as speech using any known technique. In yet another,embodiment, spoken words may be manually determined.

In operation 614, key words may be determined from the audio transcript.A key word may be a word that provides significant information contentabout a scene in a video. For example, a key word may be a name of anactor that appears in a scene. A key word may be a name of a concept oridea that is central to a plot or story. For example, the word “run” maybe a key word for the movie Forrest Gump. A key word may be a name of asong. A key word may be a word that is predefined to be objectionable orliked by a viewer. For example, a vulgar word may be predefined as a keyword. In one embodiment, a key word may be determined from the audiotranscript by counting the frequency of occurrences of words, the mostfrequently occurring verbs and nouns being determined to be key words.The operation 614 may include generating key word objects for eachdetermined key word. In addition, key word tags may be created andstored in the key word tag file 306 (shown in FIG. 3).

In one embodiment, a viewing pattern of a viewer may be gathered duringthe viewing of various videos. Using the viewing pattern, a viewingprofile for a viewer may be generated. The viewing profile may identifycategories of objects the viewer prefers. In addition, a viewer maymanually input content types that he or she prefers or findsobjectionable.

FIGS. 7 and 8 depict the display screen 402 for displaying a userinterface according to various embodiments. In one embodiment, a viewermay select one or more time segments to create a playlist. In theexample shown in FIG. 7, a viewer has selected time segments 702 and704. In this example, the viewer desires to view a playlist thatincludes time segments in which both OBJECT 2 and OBJECT 7 appear. Inone embodiment, a viewer may select a time segment using a pointingdevice, such as a mouse or a touch screen. Once a playlist has beencreated by a viewer, the Play Selected button 403 may be activated toplay the selected time segment. In addition, in one embodimentadditional time segments may be recommended to a viewer. One or moreOBJECTS in the selected segments may be automatically determined ormanually designated by a viewer. An automated search for any othersegments that include these OBJECTS may be performed. Segments that arefound to include these OBJECTS may then be recommended to a viewer. Inthe example of FIG. 8, the time segments 802 and 804 are recommended toa viewer. The time segments are segments in which both OBJECT 2 andOBJECT 7 appear.

In the foregoing, reference is made to various embodiments. It should beunderstood, however, that this disclosure is not limited to thespecifically described embodiments. Instead, any combination of thedescribed features and elements, whether related to differentembodiments or not, is contemplated to implement and practice thisdisclosure. Many modifications and variations may be apparent to thoseof ordinary skill in the art without departing from the scope and spiritof the described embodiments. Furthermore, although embodiments of thisdisclosure may achieve advantages over other possible solutions or overthe prior art, whether or not a particular advantage is achieved by agiven embodiment is not limiting of this disclosure. Thus, the describedaspects, features, embodiments, and advantages are merely illustrativeand are not considered elements or limitations of the appended claimsexcept where explicitly recited in a claim(s).

As will be appreciated by one skilled in the art, aspects of the presentdisclosure may be embodied as a system, method, or computer programproduct. Accordingly, aspects of the present disclosure may take theform of an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.), or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module,” or “system.”Furthermore, aspects of the present disclosure may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination thereof. More specificexamples (a non-exhaustive list) of the computer readable storage mediumwould include the following: an electrical connection having one or morewires, a portable computer diskette, a hard disk, a random access memory(RAM), a read-only memory (ROM), an erasable programmable read-onlymemory (EPROM or Flash memory), an optical fiber, a portable compactdisc read-only memory (CD-ROM), an optical storage device, a magneticstorage device, or any suitable combination thereof. In the context ofthis disclosure, a computer readable storage medium may be any tangiblemedium that can contain, or store, a program for use by or in connectionwith an instruction execution system, apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wire line, optical fiber cable, RF, etc., or any suitable combinationthereof.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including: an object oriented programminglanguage such as Java, Smalltalk, C++, or the like; and conventionalprocedural programming languages, such as the “C” programming languageor similar programming languages. The program code may execute asspecifically described herein. In addition, the program code may executeentirely on the viewer's computer, partly on the viewer's computer, as astand-alone software package, partly on the viewer's computer and partlyon a remote computer, or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to theviewer's computer through any type of network, including a local areanetwork (LAN) or a wide area network (WAN), or the connection may bemade to an external computer (for example, through the Internet using anInternet Service Provider).

Aspects of the present disclosure have been described with reference toflowchart illustrations, block diagrams, or both, of methods,apparatuses (systems), and computer program products according toembodiments of this disclosure. It will be understood that each block ofthe flowchart illustrations or block diagrams, and combinations ofblocks in the flowchart illustrations or block diagrams, can beimplemented by computer program instructions. These computer programinstructions may be provided to a processor of a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor of the computer or other programmabledata processing apparatus, create means for implementing the functionsor acts specified in the flowchart or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function or act specified in the flowchart or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus, or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions or acts specified in the flowchart or blockdiagram block or blocks.

Embodiments according to this disclosure may be provided to end-usersthrough a cloud-computing infrastructure. Cloud computing generallyrefers to the provision of scalable computing resources as a serviceover a network. More formally, cloud computing may be defined as acomputing capability that provides an abstraction between the computingresource and its underlying technical architecture (e.g., servers,storage, networks), enabling convenient, on-demand network access to ashared pool of configurable computing resources that can be rapidlyprovisioned and released with minimal management effort or serviceprovider interaction. Thus, cloud computing allows a user to accessvirtual computing resources (e.g., storage, data, applications, and evencomplete virtualized computing systems) in “the cloud,” without regardfor the underlying physical systems (or locations of those systems) usedto provide the computing resources.

Typically, cloud-computing resources are provided to a user on apay-per-use basis, where users are charged only for the computingresources actually used (e.g., an amount of storage space used by a useror a number of virtualized systems instantiated by the user). A user canaccess any of the resources that reside in the cloud at any time, andfrom anywhere across the Internet. In context of the present disclosure,a user may access applications or related data available in the cloud.For example, the nodes used to create a stream computing application maybe virtual machines hosted by a cloud service provider. Doing so allowsa user to access this information from any computing system attached toa network connected to the cloud (e.g., the Internet).

The flowchart and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present disclosure. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which may include one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams or flowchart illustration, andcombinations of blocks in the block diagrams or flowchart illustration,can be implemented by special purpose hardware-based systems thatperform the specified functions or acts, or combinations of specialpurpose hardware and computer instructions.

While the foregoing is directed to exemplary embodiments, other andfurther embodiments of the invention may be devised without departingfrom the basic scope thereof, and the scope thereof is determined by theclaims that follow.

What is claimed is:
 1. A computer system for rendering a summary of avideo, comprising: a processor; a display device having a displayscreen; and a memory communicatively coupled with the processor, thememory having instructions stored therein that, when executed by theprocessor, cause the computer system to: determine one or more semanticsegments of the video having metadata indicating a type of the video;determine one or more visual tags, one or more audio keyword tags, andone or more attribute tags associated with the one or more semanticsegments of the video, wherein the one or more visual tags, the one ormore audio keyword tags, and the one or more attribute tags relates tocontent of the one or more semantic segments of the video; validate thedetermined one or more visual tags, the determined one or more audiokeyword tags, and the determined one or more attribute tags by comparingeach of the determined one or more visual tags, the determined one ormore audio keyword tags, and the determined one or more attribute tagsagainst one another for each of the determined one or more semanticsegment of the video, wherein if a content identified by one of thedetermined one or more visual tags, the determined one or more audiokeyword tags, and the determined one or more attribute tags is notidentified by comparison with another of the determined one or morevisual tags, the determined one or more audio keyword tags, and thedetermined one or more attribute tags, then the content identified byone of the determined one or more visual tags, the determined one ormore audio keyword tags, and the determined one or more attribute tagsis discarded from further content analysis; generate one or more emotionobjects by identifying patterns of the determined one or more visualtags, the determined one or more audio keyword tags, and the determinedone or more attribute tags that correspond or correlate with the one ormore emotion object for each of the determined one or more semanticsegment of the video for which the content identified by one of thedetermined one or more visual tags, the determined one or more audiokeyword tags, and the determined one or more attribute tags has not beendiscarded during validation, wherein each emotion object of thegenerated one or more emotion objects specifies an emotion or a moodthat a corresponding one or more semantic segments of the video isexpected to elicit from a viewer; generate a video summary interface onthe display screen for presentation to the viewer, wherein the generatedvideo summary interface comprises of a first window display region and asecond window display region, wherein the horizontal extent of the firstwindow display region of the video summary interface corresponds withthe temporal length of a frame of the video; display the frame of thevideo within the second window display region of the video summaryinterface; display a timeline object within the first window displayregion of the video summary interface, wherein the timeline objectcorresponds to the temporal length of the frame of the video displayedin the second window display region of the video summary interface;display a first indicia associated with the generated one or moreemotion objects within the first window display region of the videosummary interface, wherein the first indicia is displayed within thefirst window display region of the video summary interface at a locationcorresponding with the temporal position appearance of the one or moreemotion object in the video; display an audio object indicia associatedwith the determined one or more audio keyword tags within the firstwindow display region of the video summary interface, wherein the audioobject indicia is displayed within the first window display region ofthe video summary interface at a temporal location corresponding withthe temporal rendering of the determined one or more audio keyword tagsin the frame of the video; and display a key word object indiciaassociated with the determined one or more audio keyword tags within thefirst window display region of the video summary interface, wherein thekey word object indicia is displayed within the first window displayregion of the video summary interface at the temporal locationcorresponding with the temporal rendering of the determined one or moreaudio keyword tags in the frame of the video.
 2. The computer system ofclaim 1, wherein the instructions when executed by the processor furthercauses the computer system to determine a visual object for at least oneof the determined one or more semantic segments of the video.
 3. Thecomputer system of claim 1, wherein the instructions when executed bythe processor further causes the computer system to determine an audioobject for at least one of the determined one or more semantic segmentsof the video.
 4. The computer system of claim 1, wherein theinstructions when executed by the processor further causes the computersystem to determine a key word object for at least one of the determinedone or more semantic segments of the video.
 5. The computer system ofclaim 1, wherein the first indicia is associated with two or moreemotion objects.
 6. The computer system of claim 1, wherein the firstindicia of the emotion object is displayed with one of a size, color, orshade corresponding to an expected intensity with which the viewer is toperceive the semantic segment.
 7. A method for rendering a summary of avideo, comprising: determining, by a processor, one or more semanticsegments of the video having metadata indicating a type of the video;determining, by the processor, one or more visual tags, one or moreaudio keyword tags, and one or more attribute tags associated with theone or more semantic segments of the video, wherein the one or morevisual tags, the one or more audio keyword tags, and the one or moreattribute tags relates to content of the one or more semantic segmentsof the video; validating, by the processor, the determined one or morevisual tags, the determined one or more audio keyword tags, and thedetermined one or more attribute tags by comparing each of thedetermined one or more visual tags, the determined one or more audiokeyword tags, and the determined one or more attribute tags against oneanother for each of the determined one or more semantic segment of thevideo, wherein if a content identified by one of the determined one ormore visual tags, the determined one or more audio keyword tags, and thedetermined one or more attribute tags is not identified by comparisonwith another of the determined one or more visual tags, the determinedone or more audio keyword tags, and the determined one or more attributetags, then the content identified by one of the determined one or morevisual tags, the determined one or more audio keyword tags, and thedetermined one or more attribute tags is discarded from further contentanalysis; generating, by the processor, one or more emotion objects byidentifying patterns of the determined one or more visual tags, thedetermined one or more audio keyword tags, and the determined one ormore attribute tags that correspond or correlate with the one or moreemotion object for each of the determined one or more semantic segmentof the video for which the content identified by one of the determinedone or more visual tags, the determined one or more audio keyword tags,and the determined one or more attribute tags has not been discardedduring validation, wherein each emotion object of the generated one ormore emotion objects specifies an emotion or a mood that a correspondingone or more semantic segments of the video is expected to elicit from aviewer; generating, by the processor, a video summary interface on thedisplay screen for presentation to the viewer, wherein the generatedvideo summary interface comprises of a first window display region and asecond window display region, wherein the horizontal extent of the firstwindow display region of the video summary interface corresponds withthe temporal length of a frame of the video; displaying, by theprocessor, the frame of the video within the second window displayregion of the video summary interface; displaying, by the processor, atimeline object within the first window display region of the videosummary interface, wherein the timeline object corresponds to thetemporal length of the frame of the video displayed in the second windowdisplay region of the video summary interface; displaying, by theprocessor, a first indicia associated with the generated one or moreemotion objects within the first window display region of the videosummary interface, wherein the first indicia is displayed within thefirst window display region of the video summary interface at a locationcorresponding with the temporal position appearance of the one or moreemotion object in the video; displaying, by the processor, an audioobject indicia associated with the determined one or more audio keywordtags within the first window display region of the video summaryinterface, wherein the audio object indicia is displayed within thefirst window display region of the video summary interface at a temporallocation corresponding with the temporal rendering of the determined oneor more audio keyword tags in the frame of the video; and displaying, bythe processor, a key word object indicia associated with the determinedone or more audio keyword tags within the first window display region ofthe video summary interface, wherein the key word object indicia isdisplayed within the first window display region of the video summaryinterface at the temporal location corresponding with the temporalrendering of the determined one or more audio keyword tags in the frameof the video.
 8. The method of claim 7, further comprising determining avisual object for at least one of the determined one or more semanticsegments of the video.
 9. The method of claim 7, further comprisingdetermining an audio object for at least one of the determined one ormore semantic segments of the video.
 10. The method of claim 7, furthercomprising determining a key word object for at least one of thedetermined one or more semantic segments of the video.
 11. The method ofclaim 7, wherein the first indicia is associated with two or moreemotion objects.
 12. A non-transitory computer-readable storage mediumhaving executable code stored thereon to cause a machine to perform amethod for rendering a summary of a video, comprising: determining, by aprocessor, one or more semantic segments of the video having metadataindicating a type of the video; determining, by the processor, one ormore visual tags, one or more audio keyword tags, and one or moreattribute tags associated with the one or more semantic segments of thevideo, wherein the one or more visual tags, the one or more audiokeyword tags, and the one or more attribute tags relates to content ofthe one or more semantic segments of the video; validating, by theprocessor, the determined one or more visual tags, the determined one ormore audio keyword tags, and the determined one or more attribute tagsby comparing each of the determined one or more visual tags, thedetermined one or more audio keyword tags, and the determined one ormore attribute tags against one another for each of the determined oneor more semantic segment of the video, wherein if a content identifiedby one of the determined one or more visual tags, the determined one ormore audio keyword tags, and the determined one or more attribute tagsis not identified by comparison with another of the determined one ormore visual tags, the determined one or more audio keyword tags, and thedetermined one or more attribute tags, then the content identified byone of the determined one or more visual tags, the determined one ormore audio keyword tags, and the determined one or more attribute tagsis discarded from further content analysis; generating, by theprocessor, one or more emotion objects by identifying patterns of thedetermined one or more visual tags, the determined one or more audiokeyword tags, and the determined one or more attribute tags thatcorrespond or correlate with the one or more emotion object for each ofthe determined one or more semantic segment of the video for which thecontent identified by one of the determined one or more visual tags, thedetermined one or more audio keyword tags, and the determined one ormore attribute tags has not been discarded during validation, whereineach emotion object of the generated one or more emotion objectsspecifies an emotion or a mood that a corresponding one or more semanticsegments of the video is expected to elicit from a viewer; generating,by the processor, a video summary interface on the display screen forpresentation to the viewer, wherein the generated video summaryinterface comprises of a first window display region and a second windowdisplay region, wherein the horizontal extent of the first windowdisplay region of the video summary interface corresponds with thetemporal length of a frame of the video; displaying, by the processor,the frame of the video within the second window display region of thevideo summary interface; displaying, by the processor, a timeline objectwithin the first window display region of the video summary interface,wherein the timeline object corresponds to the temporal length of theframe of the video displayed in the second window display region of thevideo summary interface; displaying, by the processor, a first indiciaassociated with the generated one or more emotion objects within thefirst window display region of the video summary interface, wherein thefirst indicia is displayed within the first window display region of thevideo summary interface at a location corresponding with the temporalposition appearance of the one or more emotion object in the video;displaying, by the processor, an audio object indicia associated withthe determined one or more audio keyword tags within the first windowdisplay region of the video summary interface, wherein the audio objectindicia is displayed within the first window display region of the videosummary interface at a temporal location corresponding with the temporalrendering of the determined one or more audio keyword tags in the frameof the video; and displaying, by the processor, a key word objectindicia associated with the determined one or more audio keyword tagswithin the first window display region of the video summary interface,wherein the key word object indicia is displayed within the first windowdisplay region of the video summary interface at the temporal locationcorresponding with the temporal rendering of the determined one or moreaudio keyword tags in the frame of the video.
 13. The non-transitorycomputer-readable storage medium of claim 12, wherein the method furthercomprises determining a visual object for at least one of the determinedone or more semantic segments of the video.
 14. The non-transitorycomputer-readable storage medium of claim 12, wherein the method furthercomprises determining an audio object for at least one of the determinedone or more semantic segments of the video.
 15. The non-transitorycomputer-readable storage medium of claim 12, wherein the method furthercomprises determining a key word object for at least one of thedetermined one or more semantic segments of the video.
 16. Thenon-transitory computer-readable storage medium of claim 12, wherein thefirst indicia are associated with two or more emotion objects.